Bridge inspection using unmanned aerial vehicles (UAV) with high performance vision sensors has received considerable attention due to its safety and reliability. As bridges become obsolete, the number of bridges that need to be inspected increases, and they require much maintenance cost. Therefore, a bridge inspection method based on UAV with vision sensors is proposed as one of the promising strategies to maintain bridges. In this paper, a crack identification method by using a commercial UAV with a high resolution vision sensor is investigated in an aging concrete bridge. First, a point cloud-based background model is generated in the preliminary flight. Then, cracks on the structural surface are detected with the deep learning algorithm, and their thickness and length are calculated. In the deep learning method, region with convolutional neural networks (R-CNN)-based transfer learning is applied. As a result, a new network for the 384 collected crack images of 256 × 256 pixel resolution is generated from the pre-trained network. A field test is conducted to verify the proposed approach, and the experimental results proved that the UAV-based bridge inspection is effective at identifying and quantifying the cracks on the structures.

To withstand harsh conditions and have a moderate strength, it is desirable to use natural rubber for base isolators. In addition, previous studies have measured the magnetorheological (MR) effect under low-strain range, mostly within 10%. In the reality, it is necessary to evaluate the performance under large-strain range for base isolators. In this study, material properties of natural rubberbased MREs with various mixing ratios were evaluated under large-strain range (∼100%). In the first step, MREs with various iron ratios were fabricated and evaluated to observe the MR effect according to the ratio and arrangement of iron powder. As a result, the highest MR effect (22.0% at 100% strain) and damping ratio (10.29%) were observed in the sample with 35% iron ratio, and the MR effect of the isotropic and the anisotropic MRE did not show significant difference under large-strain (50∼100%). In the second step, MRE samples containing the optimum iron ratio (investigated in the first step) and various mixing ratios of carbon black and naphthenic oil were prepared. As a result, the MRE containing 60phr of carbon black and 40phr of naphthenic oil had the highest MR effect (33.8% at 100% strain). Compared to the case without additives, it showed an obvious improvement.

The Internet of Things (IoT) has been implemented to provide solutions for certain event detection because of ease of installation, computing and communication capability, and cost-effectiveness. Seismic event detection, however, is still a challenge due to a lack of high-fidelity sensing and classification efficiency. This study proposes BLESeis, an IoT sensor for smart earthquake detection. BLESeis comprises three main parts: (1) high-fidelity vibration sensing using a MEMS accelerometer and digital filtering; (2) an embedded earthquake detection algorithm; (3) BLE (Bluetooth low energy) beacon for earthquake notification. For high-fidelity vibration sensing, a triggering algorithm and embedded finite impulse response (FIR) low-pass filter are developed. The acquired vibration is then classified by the earthquake detection algorithm developed to identify the earthquake signal from other vibration sources using time and frequency domain analysis. Upon detection of an earthquake, the BLE beacon broadcasts using the proposed data packet for efficient notification and visualization. The performance of the proposed system is evaluated through numerical simulations and a set of experiments using shaking table tests. The experiments show the feasibility of the low-cost earthquake detection and notification system.

Summary All civil infrastructure, including bridges, deteriorates over time. Unmanned aerial vehicle (UAV) based visual inspection of bridges has been proposed to assess the condition of bridges. However, existing methods cannot determine the seismic performance of bridges based on the results of UAV‐based visual inspection. In this study, a novel approach is proposed to assess the seismic performance of deteriorated bridges with the results of UAV‐based damage detection. The proposed approach consists of two phases: (i) the damage detection phase using a UAV and (ii) the seismic performance assessment phase. The images obtained from UAV survey are used to conduct condition assessment for the bridge, based on a previously developed region‐based convolutional neural network (R‐CNN), and the damage grade is assigned. Note that here damage includes both seismic damage and deterioration. Subsequently, the finite element (FE) model of the intact bridge is updated to correspond to the assigned damage index. To demonstrate the proposed approach, an in‐service prestressed concrete box‐girder bridge is investigated. In particular, the seismic response of the deteriorated bridge is assessed based on a comparison with the intact bridge responses; focus is placed on the maximum moment and maximum displacement at the pier and the girder. Predictions indicate that the seismic responses of the deteriorated in‐service bridge are 10% poorer than those of the intact bridge. These results demonstrate the potential for the UAV‐based approach for evaluating the seismic performance of deteriorated bridges.

This paper reports the theoretical findings of the new modified type of tuned liquid column ball damper (TLCBD), called a tuned liquid column ball spring damper (TLCBSD). In this new modified form, the ball inside the horizontal section of the damper is attached to the spring. Furthermore, two types of this modified version are proposed, known as a tuned liquid column ball spring sliding damper (TLCBSSD) and a tuned liquid column ball spring rolling damper (TLCBSRD). In the former, the rotational motion of the ball attached to the spring is restricted, whereas in the latter, the ball attached to the spring can translate as well as rotate. Mathematical models and optimum design parameters are formulated for both types. The performance of these new modified damper versions is assessed numerically and subjected to harmonic, seismic, and impulse loadings. The results show that the performance of the newly proposed dampers is relatively better than traditional TLCBDs in harmonic and seismic excitations. The peak response reduction soon after the impact load becomes zero is comparatively better in TLCBSDs over TLCBDs. Overall, the newly proposed passive vibration control devices performed excellently in structure response reduction over TLCBDs.